Prediction of concrete fatigue durability using Bayesian neural networks
Abstract
The utility of Bayesian neural networks to predict concrete fatigue durability as a function of concrete mechanical parameters of a specimen and characteristics of the loading cycle is investigated. Bayesian approach to learning neural networks allows automatic control of the complexity of the non-linear model, calculation of error bars and automatic determination of the relevance of various input variables. Comparative results on experimental data set show that Bayesian neural network works well.
Keywords
Bayesian neural networks, concrete fatigue durability, prediction,References
[1] C.A.L. Bailer-Jones, T.J. Sabin, D.J.C. MacKay, and P.J. Withers. Prediction of deformed and annealed microstructures using Bayesian neural networks and Gaussian processes. In: Proc. of the Australia-Pacific Forum on Intelligent Processing and Manufacturing of Materials. 1997. See http://www.mpia-hd.mpg.de/homes/ calj/.[2] C.M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Oxford, 1995. See http://research.microsoft.com/~cmbishop/.
[3] K. Furtak. Strength of the concrete under multiple repeated loads (in Polish). Arch. of Civil Eng., 30, 1984.
[4] D. Husmeier. Neural Networks for Conditional Probability Estimation: Forecasting Beyond Point Predictions. Perspectives in Neural Computing. Springer London, 1999.
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Published
Nov 30, 2022
How to Cite
SŁOŃSKI, Marek.
Prediction of concrete fatigue durability using Bayesian neural networks.
Computer Assisted Methods in Engineering and Science, [S.l.], v. 12, n. 2-3, p. 259-265, nov. 2022.
ISSN 2956-5839.
Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/994>. Date accessed: 02 may 2024.
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